Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account. To do so, the predictions are modelled as a graphical model, which represents the presence of dependencies between the predictions. The kind of graph used depends on the application. For example, in natural language processing, "linear chain" CRFs are popular, for which each prediction is dependent only on its immediate neighbours. In image processing, the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions. Other examples where CRFs are used are: labeling or parsing of sequential data for natural language processing or biological sequences, part-of-speech tagging, shallow parsing, named entity recognition, gene finding, peptide critical functional region finding, and object recognition and image segmentation in computer vision. == Description == CRFs are a type of discriminative undirected probabilistic graphical model. Lafferty, McCallum and Pereira define a CRF on observations X {\displaystyle {\boldsymbol {X}}} and random variables Y {\displaystyle {\boldsymbol {Y}}} as follows: Let G = ( V , E ) {\displaystyle G=(V,E)} be a graph such that Y = ( Y v ) v ∈ V {\displaystyle {\boldsymbol {Y}}=({\boldsymbol {Y}}_{v})_{v\in V}} , so that Y {\displaystyle {\boldsymbol {Y}}} is indexed by the vertices of G {\displaystyle G} . Then ( X , Y ) {\displaystyle ({\boldsymbol {X}},{\boldsymbol {Y}})} is a conditional random field when each random variable Y v {\displaystyle {\boldsymbol {Y}}_{v}} , conditioned on X {\displaystyle {\boldsymbol {X}}} , obeys the Markov property with respect to the graph; that is, its probability is dependent only on its neighbours in G and not its past states: P ( Y v | X , { Y w : w ≠ v } ) = P ( Y v | X , { Y w : w ∼ v } ) {\displaystyle P({\boldsymbol {Y}}_{v}|{\boldsymbol {X}},\{{\boldsymbol {Y}}_{w}:w\neq v\})=P({\boldsymbol {Y}}_{v}|{\boldsymbol {X}},\{{\boldsymbol {Y}}_{w}:w\sim v\})} , where w ∼ v {\displaystyle {\mathit {w}}\sim v} means that w {\displaystyle w} and v {\displaystyle v} are neighbors in G {\displaystyle G} . What this means is that a CRF is an undirected graphical model whose nodes can be divided into exactly two disjoint sets X {\displaystyle {\boldsymbol {X}}} and Y {\displaystyle {\boldsymbol {Y}}} , the observed and output variables, respectively; the conditional distribution p ( Y | X ) {\displaystyle p({\boldsymbol {Y}}|{\boldsymbol {X}})} is then modeled. === Inference === For general graphs, the problem of exact inference in CRFs is intractable. The inference problem for a CRF is basically the same as for an MRF and the same arguments hold. However, there exist special cases for which exact inference is feasible: If the graph is a chain or a tree, message passing algorithms yield exact solutions. The algorithms used in these cases are analogous to the forward-backward and Viterbi algorithm for the case of HMMs. If the CRF only contains pair-wise potentials and the energy is submodular, combinatorial min cut/max flow algorithms yield exact solutions. If exact inference is impossible, several algorithms can be used to obtain approximate solutions. These include: Loopy belief propagation Alpha expansion Mean field inference Linear programming relaxations === Parameter learning === Learning the parameters θ {\displaystyle \theta } is usually done by maximum likelihood learning for p ( Y i | X i ; θ ) {\displaystyle p(Y_{i}|X_{i};\theta )} . If all nodes have exponential family distributions and all nodes are observed during training, this optimization is convex. It can be solved for example using gradient descent algorithms, or Quasi-Newton methods such as the L-BFGS algorithm. On the other hand, if some variables are unobserved, the inference problem has to be solved for these variables. Exact inference is intractable in general graphs, so approximations have to be used. === Examples === In sequence modeling, the graph of interest is usually a chain graph. An input sequence of observed variables X {\displaystyle X} represents a sequence of observations and Y {\displaystyle Y} represents a hidden (or unknown) state variable that needs to be inferred given the observations. The Y i {\displaystyle Y_{i}} are structured to form a chain, with an edge between each Y i − 1 {\displaystyle Y_{i-1}} and Y i {\displaystyle Y_{i}} . As well as having a simple interpretation of the Y i {\displaystyle Y_{i}} as "labels" for each element in the input sequence, this layout admits efficient algorithms for: model training, learning the conditional distributions between the Y i {\displaystyle Y_{i}} and feature functions from some corpus of training data. decoding, determining the probability of a given label sequence Y {\displaystyle Y} given X {\displaystyle X} . inference, determining the most likely label sequence Y {\displaystyle Y} given X {\displaystyle X} . The conditional dependency of each Y i {\displaystyle Y_{i}} on X {\displaystyle X} is defined through a fixed set of feature functions of the form f ( i , Y i − 1 , Y i , X ) {\displaystyle f(i,Y_{i-1},Y_{i},X)} , which can be thought of as measurements on the input sequence that partially determine the likelihood of each possible value for Y i {\displaystyle Y_{i}} . The model assigns each feature a numerical weight and combines them to determine the probability of a certain value for Y i {\displaystyle Y_{i}} . Linear-chain CRFs have many of the same applications as conceptually simpler hidden Markov models (HMMs), but relax certain assumptions about the input and output sequence distributions. An HMM can loosely be understood as a CRF with very specific feature functions that use constant probabilities to model state transitions and emissions. Conversely, a CRF can loosely be understood as a generalization of an HMM that makes the constant transition probabilities into arbitrary functions that vary across the positions in the sequence of hidden states, depending on the input sequence. Notably, in contrast to HMMs, CRFs can contain any number of feature functions, the feature functions can inspect the entire input sequence X {\displaystyle X} at any point during inference, and the range of the feature functions need not have a probabilistic interpretation. == Variants == === Higher-order CRFs and semi-Markov CRFs === CRFs can be extended into higher order models by making each Y i {\displaystyle Y_{i}} dependent on a fixed number k {\displaystyle k} of previous variables Y i − k , . . . , Y i − 1 {\displaystyle Y_{i-k},...,Y_{i-1}} . In conventional formulations of higher order CRFs, training and inference are only practical for small values of k {\displaystyle k} (such as k ≤ 5), since their computational cost increases exponentially with k {\displaystyle k} . However, another recent advance has managed to ameliorate these issues by leveraging concepts and tools from the field of Bayesian nonparametrics. Specifically, the CRF-infinity approach constitutes a CRF-type model that is capable of learning infinitely-long temporal dynamics in a scalable fashion. This is effected by introducing a novel potential function for CRFs that is based on the Sequence Memoizer (SM), a nonparametric Bayesian model for learning infinitely-long dynamics in sequential observations. To render such a model computationally tractable, CRF-infinity employs a mean-field approximation of the postulated novel potential functions (which are driven by an SM). This allows for devising efficient approximate training and inference algorithms for the model, without undermining its capability to capture and model temporal dependencies of arbitrary length. There exists another generalization of CRFs, the semi-Markov conditional random field (semi-CRF), which models variable-length segmentations of the label sequence Y {\displaystyle Y} . This provides much of the power of higher-order CRFs to model long-range dependencies of the Y i {\displaystyle Y_{i}} , at a reasonable computational cost. Finally, large-margin models for structured prediction, such as the structured Support Vector Machine can be seen as an alternative training procedure to CRFs. === Latent-dynamic conditional random field === Latent-dynamic conditional random fields (LDCRF) or discriminative probabilistic latent variable models (DPLVM) are a type of CRFs for sequence tagging tasks. They are latent variable models that are trained discriminatively. In an LDCRF, like in any sequence tagging task, given a sequence of observations x = x 1 , … , x n {\displaystyle x_{1},\dots ,x_{n}} , the main problem the model must solve is how to assign a sequence of labels y = y 1 , … , y n {\displaystyle y_{1},\dots ,y_{n}} from one finite set
Amaq News Agency
Amaq News Agency (Arabic: وكالة أعماق الإخبارية, romanized: Wakālat Aʻmāq al-Ikhbārīyah) is a news outlet linked to the Islamic State (IS). Amaq is often the "first point of publication for claims of responsibility" for terrorist attacks in Western countries by the Islamic State. In March 2019, Amaq News Agency was designated as a foreign terrorist organization by the United States Department of State. == History == Among the founders of Amaq was Syrian journalist Baraa Kadek, who joined IS in late 2013, Abu Muhammad al-Furqan, and seven others who originally worked for Halab News Network. According to The New York Times, it has a direct connection with IS, from which it "gets tips". Its name was taken from Amik Valley in Hatay Province, which is mentioned in a hadith as the site of an "apocalyptic victory over non-believers". Amaq News Agency was first noticed by SITE during the Siege of Kobanî (Syria) in 2014, when its updates were shared among IS fighters. It became more widely known after it began reporting claims of responsibility for terrorist attacks in Western countries, such as the 2015 San Bernardino attack, for which IS officially claimed responsibility the next day. An Amaq cameraman shot the first footage of the capture of Palmyra in 2015. Amaq launched an official mobile app in 2015 and has warned against unofficial versions that reportedly have been used to spy on its users. It also uses a Telegram account. It had a WordPress-based blog, but it was removed without explanation in April 2016. On 12 June 2016, IS claimed responsibility for the Pulse nightclub shooting through Amaq, without prior knowledge of the attack. The shooter, Omar Mateen had later pledged allegiance to IS via a phone call with emergency services. On 31 May 2017, a Facebook post announced Amaq's founder, Baraa Kadek AKA Rayan Meshaal, had been killed with his daughter by an American airstrike on Mayadin. The post was reportedly made by his younger brother. Reuters could not immediately verify this account. On 27 July 2017, the US confirmed that Kadek had been killed by a coalition airstrike near Mayadin between 25 and 27 May 2017. In June 2017, German police arrested a 23-year-old Syrian man identified only as Mohammed G., accusing him of communicating with the alleged perpetrator of the 2016 Malmö Muslim community centre arson in order to report to Amaq. On 21 March 2019, the U.S. Department of State officially deemed Amaq an alias of IS, and thus a Foreign Terrorist Organization. On 22 March 2024, the Islamic State claimed responsibility for the Crocus City Hall attack through Amaq, U.S. officials confirmed the claim shortly after. A day after the attack, Amaq published a video of the attack, filmed by one of the attackers. It showed the attackers shooting victims and slitting the throat of another, while the filming attacker praises Allah and speaks against infidels. == Character == Amaq publishes a stream of short news reports, both text and video, on the mobile app Telegram. The reports take on the trappings of mainstream journalism, with "Breaking News" headings, and embedded reporters at the scenes of IS battles. The reports try to appear neutral, toning down the jihadist language and sectarian slurs IS uses in its official releases. Charlie Winter of the Transcultural Conflict and Violence Initiative at Georgia State University, and Rita Katz of SITE Intelligence Group in Washington say Amaq functions much like the state-owned news agency of IS, though the group does not acknowledge it as such. Katz said it behaves "like a state media". Amaq appears to have been allowed to develop by IS as a way to have a news outlet that is controlled by the group but is somewhat removed from it, giving IS more of the appearance of legitimacy. == Reliability == According to Rukmini Callimachi in The New York Times: "Despite a widespread view that the Islamic State opportunistically claims attacks with which it has little genuine connection, its track record—minus a handful of exceptions—suggests a more rigorous protocol. At times, the Islamic State has got details wrong, or inflated casualty figures, but the gist of its claims is typically correct." According to Callimachi, the group considers itself responsible for acts carried out by people who were inspired by its propaganda, as well as acts carried out by its own personnel and in some instances, had claimed attacks before the identities of the killers were known. Graeme Wood writing in The Atlantic in October 2017, wrote "The idea that the Islamic State simply scans the news in search of mass killings, then sends out press releases in hope of stealing glory, is false. Amaq may learn details of the attacks from mainstream media ... but its claim of credit typically flows from an Amaq-specific source." An October 2017 article in The Hill, points to two false claims made in the summer of 2017, the Resorts World Manila attack and a false claim that bombs had been planted at Charles de Gaulle Airport in Paris. Also, a claimed IS connection to the 2017 Las Vegas shooting proved to be false. According to Rita Katz on the SITE Intelligence Group website, calling a terrorist a "soldier of the caliphate (warrior from the caliphate)" in a statement issued by Amaq, was the usual way in which IS indicated that it inspired an attack. Centrally coordinated attacks were usually described as "executed by a detachment belonging to the Islamic State", and were often announced by both Amaq and by IS' central media command. == Online presence == In November 2019, Belgian police said they had carried out a successful cyberattack on Amaq, thus leaving IS without an operational communication channel. However, Amaq has since regained online presence, primarily on dark web platforms to make it harder for law enforcement to take them down without physical access to the server hosting the specific platform.
Deepfake
Deepfakes (a portmanteau of 'deep learning' and 'fake') are images, videos, or audio that have been edited or generated using artificial intelligence, AI-based tools or audio-video editing software. They may depict real or fictional people and are considered a form of synthetic media, that is media that is usually created by artificial intelligence systems by combining various media elements into a new media artifact. While the act of creating fake content is not new, deepfakes uniquely leverage machine learning and artificial intelligence techniques, including facial recognition algorithms and artificial neural networks such as variational autoencoders and generative adversarial networks (GANs). In turn, the field of image forensics has worked to develop techniques to detect manipulated images. Deepfakes have garnered widespread attention for their potential use in creating child sexual abuse material, celebrity pornographic videos, revenge porn, fake news, hoaxes, bullying, and financial fraud. Academics have raised concerns about the potential for deepfakes to promote disinformation and hate speech, as well as interfere with elections. In response, the information technology industry and governments have proposed recommendations and methods to detect and mitigate their use. Academic research has also delved deeper into the factors driving deepfake engagement online as well as potential countermeasures to malicious application of deepfakes. From traditional entertainment to gaming, deepfake technology has evolved to be increasingly convincing and available to the public, allowing for the disruption of the entertainment and media industries. == History == Photo manipulation was developed in the 19th century and soon applied to motion pictures. Technology steadily improved during the 20th century, and more quickly with the advent of digital video. Deepfake technology has been developed by researchers at academic institutions beginning in the 1990s, and later by amateurs in online communities. More recently, the methods have been adopted by industry. The development of generative adversarial networks (GANs) in the mid-2010s represented a key technical turning point in the evolution of deepfakes. GANs allowed for the creation of highly realistic fake images and videos by training competing neural networks, achieving a much improved visual fidelity over previous methods of creating the content using rules or by using autoencoders, and formed the basis for modern deepfake methods. === Academic research === Academic research related to deepfakes is split between the field of computer vision, a sub-field of computer science, which develops techniques for creating and identifying deepfakes, and humanities and social science approaches that study the social, ethical, aesthetic implications as well as journalistic and informational implications of deepfakes. As deepfakes have risen in prominence in popularity with innovations provided by AI tools, significant research has gone into detection methods and defining the factors driving engagement with deepfakes on the internet. Deepfakes have been shown to appear on social media platforms and other parts of the internet for purposes ranging from entertainment and education related to deepfakes to misinformation to elicit strong reactions. There are gaps in research related to the propagation of deepfakes on social media. Negativity and emotional response are the primary driving factors for users sharing deepfakes. === Social science and humanities approaches to deepfakes === In cinema studies, deepfakes illustrate how "the human face is emerging as a central object of ambivalence in the digital age". Video artists have used deepfakes to "playfully rewrite film history by retrofitting canonical cinema with new star performers". Film scholar Christopher Holliday analyses how altering the gender and race of performers in familiar movie scenes destabilizes gender classifications and categories. The concept of "queering" deepfakes is also discussed in Oliver M. Gingrich's discussion of media artworks that use deepfakes to reframe gender, including British artist Jake Elwes' Zizi: Queering the Dataset, an artwork that uses deepfakes of drag queens to intentionally play with gender. The aesthetic potentials of deepfakes are also beginning to be explored. Theatre historian John Fletcher notes that early demonstrations of deepfakes are presented as performances, and situates these in the context of theater, discussing "some of the more troubling paradigm shifts" that deepfakes represent as a performance genre. While most English-language academic studies of deepfakes focus on the Western anxieties about disinformation and pornography, digital anthropologist Gabriele de Seta has analyzed the Chinese reception of deepfakes, which are known as huanlian, which translates to "changing faces". The Chinese term does not contain the "fake" of the English deepfake, and de Seta argues that this cultural context may explain why the Chinese response has centered on practical regulatory measures to "fraud risks, image rights, economic profit, and ethical imbalances". === Computer science research on deepfakes === A landmark early project was the "Video Rewrite" program, published in 1997. The program modified existing video footage of a person speaking to depict that person mouthing the words from a different audio track. It was the first system to fully automate this kind of facial reanimation, and it did so using machine learning techniques to make connections between the sounds produced by a video's subject and the shape of the subject's face. Contemporary academic projects have focused on creating more realistic videos and improving deepfake techniques. The "Synthesizing Obama" program, published in 2017, modifies video footage of former president Barack Obama to depict him mouthing the words contained in a separate audio track. The project lists as a main research contribution to its photorealistic technique for synthesizing mouth shapes from audio. The "Face2Face" program, published in 2016, modifies video footage of a person's face to depict them mimicking another person's facial expressions. The project highlights its primary research contribution as the development of the first method for re-enacting facial expressions in real time using a camera that does not capture depth, enabling the technique to work with common consumer cameras. Researchers have also shown that deepfakes are expanding into other domains such as medical imagery. In this work, it was shown how an attacker can automatically inject or remove lung cancer in a patient's 3D CT scan. The result was so convincing that it fooled three radiologists and a state-of-the-art lung cancer detection AI. To demonstrate the threat, the authors successfully performed the attack on a hospital in a White hat penetration test. A survey of deepfakes, published in May 2020, provides a timeline of how the creation and detection of deepfakes have advanced over the last few years. The survey identifies that researchers have been focusing on resolving the following challenges of deepfake creation: Generalization. High-quality deepfakes are often achieved by training on hours of footage of the target. This challenge is to minimize the amount of training data and the time to train the model required to produce quality images and to enable the execution of trained models on new identities (unseen during training). Paired Training. Training a supervised model can produce high-quality results, but requires data pairing. This is the process of finding examples of inputs and their desired outputs for the model to learn from. Data pairing is laborious and impractical when training on multiple identities and facial behaviors. Some solutions include self-supervised training (using frames from the same video), the use of unpaired networks such as Cycle-GAN, or the manipulation of network embeddings. Identity leakage. This is where the identity of the driver (i.e., the actor controlling the face in a reenactment) is partially transferred to the generated face. Some solutions proposed include attention mechanisms, few-shot learning, disentanglement, boundary conversions, and skip connections. Occlusions. When part of the face is obstructed with a hand, hair, glasses, or any other item then artifacts can occur. A common occlusion is a closed mouth which hides the inside of the mouth and the teeth. Some solutions include image segmentation during training and in-painting. Temporal coherence. In videos containing deepfakes, artifacts such as flickering and jitter can occur because the network has no context of the preceding frames. Some researchers provide this context or use novel temporal coherence losses to help improve realism. As the technology improves, the interference is diminishing. Overall, deepfakes are expected to have several implications in media and society, med
IJCAI Computers and Thought Award
The IJCAI Computers and Thought Award is presented every two years by the International Joint Conference on Artificial Intelligence (IJCAI), recognizing outstanding young scientists in artificial intelligence. It was originally funded with royalties received from the book Computers and Thought (edited by Edward Feigenbaum and Julian Feldman), and is currently funded by IJCAI. It is considered to be "the premier award for artificial intelligence researchers under the age of 35". == Laureates == Terry Winograd (1971) Patrick Winston (1973) Chuck Rieger (1975) Douglas Lenat (1977) David Marr (1979) Gerald Sussman (1981) Tom Mitchell (1983) Hector Levesque (1985) Johan de Kleer (1987) Henry Kautz (1989) Rodney Brooks (1991) Martha E. Pollack (1991) Hiroaki Kitano (1993) Sarit Kraus (1995) Stuart Russell (1995) Leslie Kaelbling (1997) Nicholas Jennings (1999) Daphne Koller (2001) Tuomas Sandholm (2003) Peter Stone (2007) Carlos Guestrin (2009) Andrew Ng (2009) Vincent Conitzer (2011) Malte Helmert (2011) Kristen Grauman (2013) Ariel D. Procaccia (2015) Percy Liang (2016) for his contributions to both the approach of semantic parsing for natural language understanding and better methods for learning latent-variable models, sometimes with weak supervision, in machine learning. Devi Parikh (2017) Stefano Ermon (2018) Guy Van den Broeck (2019) for his contributions to statistical and relational artificial intelligence, and the study of tractability in learning and reasoning. Piotr Skowron (2020) for his contributions to computational social choice, and to the theory of committee elections. Fei Fang (2021) for her contributions to integrating machine learning with game theory and the use of these novel techniques to tackle societal challenges such as more effective deployment of security resources, enhancing environmental sustainability, and reducing food insecurity. Bo Li (2022) for her contributions to uncovering the underlying connections among robustness, privacy, and generalization in AI, showing how different models are vulnerable to malicious attacks, and how to eliminate these vulnerabilities using mathematical tools that provide robustness guarantees for learning models and privacy protection. Pin-Yu Chen (2023) for his contributions to consolidating properties of trust, robustness and safety into rigorous algorithmic procedures and computable metrics for improving AI systems. Nisarg Shah (2024) for his contributions to AI and society, in particular foundational work on the theory of algorithmic fairness using principles from social choice theory. Aditya Grover (2025) for his foundational contributions uniting deep generative models, representation learning, and reinforcement learning, and for their applications in advancing scientific reasoning.
Perry Rhodan
Perry Rhodan is a German space opera franchise, named after its hero. It commenced in 1961 and has been ongoing for decades, written by an ever-changing team of authors. Having sold approximately two billion copies (in novella format) worldwide (including over one billion in Germany alone), it is the most successful science fiction book series ever written. The first billion of worldwide sales was celebrated in 1986. The series has spun off into comic books, audio dramas, video games and the like. A reboot, Perry Rhodan NEO, was launched in 2011 and began publication in English in April 2021. == Print publication == The series has spun off into many different forms of media, but originated as a serial novella published weekly since 8 September 1961 in the Romanheft (Meaning "Magazine novel") format. These are digest-sized booklets, usually containing 66 pages, the German equivalent of the now-defunct (and generally longer) American pulp magazine. They are published by Pabel-Moewig Verlag, a subsidiary of Bauer Media Group headquartered in Hamburg. As of February 2019, 3000 booklet novels of the original series, 850 spinoff novels of the sister series Atlan and over 400 paperbacks and 200 hardcover editions have been published, totalling over 300,000 pages. == English translation == The first 126 novels (plus five novels of the spinoff series Atlan) were translated into English and published by Ace Books between 1969 and 1978, with the same translations used for the British edition published by Futura Publications which issued only 39 novels. When Ace cancelled its translation of the series, translator Wendayne Ackerman self-published the following 19 novels (under the business name 'Master Publications') and made them available by subscription only. Financial disputes with the German publishers led to the cancellation of the American translation in 1979. An attempt to revive the series in English was made in 1997–1998 by Vector Publications of the US, which published translations of four issues (1800–1803) from the current storyline being published in Germany at the time. The series and its spin-offs have captured a substantial fraction of the original German science fiction output and exert influence on many German writers in the field. == Structure == The series is told in an arc storyline structure. An arc—called a "cycle"—would have anywhere from 25 to 100 issues devoted to it. Similar subsequent cycles are referred to as a "grand-cycle". == History == ‘Perry Rhodan, der Erbe des Universums’ (Eng: ‘The Heir to the Universe’, though the American/British editions instead used the subtitle 'Peacelord of the Universe') was created by German science fiction authors K. H. Scheer and Walter Ernsting and launched in 1961 by German publishing house Arthur Moewig Verlag (now Pabel-Moewig Verlag). Originally planned as a 30 to 50 volume series, it has been published continuously every week since, celebrating the 3000th issue in 2019. Written by an ever-changing team of authors, many of whom, however, remained with the series for decades or life, Perry Rhodan is issued in weekly novella-size installments in the traditional German Heftroman (pulp booklet) format. Unlike most German Heftromane, Perry Rhodan consists not of unconnected novels but is a series with a continuous, increasingly complex plotline, with frequent back references to events. In addition to its original Heftroman form, the series now also appears in hardcovers, paperbacks, e-books, comics and audiobooks. Over the decades there have also been comic strips, numerous collectibles, several encyclopedias, audio plays, inspired music, etc. The series has seen partial translations into several languages. It also spawned the German-Italian-Spanish 1967 movie Mission Stardust, which is widely considered so terrible that many fans of the series pretend it never existed. Coinciding with the 50th-anniversary World Con, on 30 September 2011, a new series named Perry Rhodan Neo began publication, attracting new readers with a reboot of the story, starting in the year 2036 instead of 1971, and a related but independent story-line. On 2 April 2021, light novel and manga publisher J-Novel Club announced Perry Rhodan NEO as a launch title for its new J-Novel Pulp imprint, making this the first ongoing English release of new Perry Rhodan serials in over 20 years. It has become the most popular science fiction book series of all time. == Overview == === Fictional history === The story begins in 1971. During the first human Moon landing by US Space Force Major Perry Rhodan and his crew, they discover a marooned extraterrestrial space ship from the fictional planet Arkon, located in the (real) M13 cluster. Appropriating the Arkonide technology, they proceed to unify Terra and carve out a place for humanity in the galaxy and the cosmos. Two of the accomplishments that enable them to do so are positronic brains and starship drives for near-instantaneous hyperspatial translation. These were directly borrowed from Isaac Asimov's science fiction. As the series progresses, major characters, including the title character, are granted relative immortality. They are immune to age and disease, but not to violent death. The story continues over the course of millennia and includes flashbacks thousands and even millions of years into the past. The scope widens to encompass other galaxies, even more remote regions of space, parallel universes and cosmic structures, time travel, paranormal powers, a variety of aliens ranging from threatening to endearing, and bodiless entities, some of which have godlike powers. === Multiverse === The universe in which the main plot generally takes place is called the Einstein Universe (or "Meekorah"). Its laws are for the most part identical to those of the real universe, as known by late 20th century science. Newer theories about dark matter and dark energy are currently not used in the series. The laws of nature follow old theories that have been disproven, in order to protect series continuity. There are many other universes, each to a greater or lesser extent different from the familiar one, in which, for example one in which time runs slower, an anti-matter universe, a shrinking universe, etc. Each universe possesses its owntimelines, which are for the most part unreachable from each other but may be accessed by special means, thereby itself creating many more parallel timelines. The Einstein Universe is embedded in a high-dimensional manifold, called Hyperspace. This hyperspace consists of several subspaces use for faster-than-light travel by technological means. The exact traits of those higher dimensions are got yhr mode pity unexplained. The border of the universe is a dimension called the deep, once used for construction of the gigantic disc-shaped world Deepland. === Psionic Web and Moral Code === The Psionic Web crosses the whole universe, constantly emitting "vital energy" and "psionic energy", guaranteeing normal (organic among others) life and the wellbeing of higher entities. The Moral Code crosses through all universes, and is linked to the Psionic Web. It is subdivided into the Cosmogenes, which are again subdivided into the Cosmonucleotids. The Cosmonucleotids determine reality and fate for their respective parts of a given universe, via messengers. Higher beings are trying to gain control of this Code to rule reality. The Moral Code itself was not installed by the higher beings, the higher powers by themselves have no clue why or by whom the Code was made. Once the Cosmocrats ordered Perry Rhodan to find the answer to the third ultimate question: "Who initiated the LAW and what does it accomplish?" Perry Rhodan had the chance to receive the answer at the mountain of creation, but refused, as he knew that the answer would destroy his mind. The negative Superintelligence Koltoroc had received the answer to the last ultimate question, 69 million years BC at Negane Mountain, but it is not known if it made any use of the information. === Onion-shell model === An evolutionary schema, similar to the Great Chain of Being, called the "onion-shell model" is employed in relationship to all life. Here, continuous evolution is from lower to higher lifeforms, culminating in bodiless entities. Later in the series, further lifeforms, representing stages between the known shells, were introduced. The main shells are: Lifeless matter Bacteria Higher animals Intelligent species Intelligent species that have contacted other species Superintelligences (SI) Matter sources/ Matter sinks Cosmocrats / Chaotarchs (High Powers) Powers close to the "Horizon of the LAW", the essence of the Multiverse The Superintelligences are the next step above normal minds. They can be born, for example, when a species collectively gives up its bodies and unites their spirits. Such Superintelligences may claim as their domain areas consisting of up to several galaxies (the entity known as "E
Tiimo
Tiimo is an app designed to help neurodivergent individuals with planning their life. In August 2024 the company raised €1.4 million, bringing their total funding to €4.3 million. At that point they had over 500,000 users, including 50,000 paid users. The app has Apple Watch support and a learning platform that includes courses on well-being and neurodiversity. The app was founded by Helene Lassen Nørlem and Melissa Würtz Azari in 2015. After being a finalist in 2024, in December 2025 Tiimo was won Apple’s iPhone App of the Year. The premium version is $10/mo and features an AI chatbot alongside the daily planner.
The Matrix (franchise)
The Matrix is an American cyberpunk media franchise consisting of four feature films, beginning with The Matrix (1999) and continuing with three sequels, Reloaded (2003), Revolutions (2003), and Resurrections (2021). The first three films were written and directed by the Wachowskis and produced by Joel Silver. The screenplay for the fourth film was written by Lana Wachowski, David Mitchell and Aleksandar Hemon, was directed by Lana Wachowski, and was produced by Grant Hill, James McTeigue, and Lana Wachowski. The franchise is owned by Warner Bros., which distributed the films along with Village Roadshow Pictures. The latter, along with Silver Pictures, are the two production companies that worked on the first three films. The series features a cyberpunk story of the technological fall of humanity, in which the creation of artificial intelligence led the way to a race of powerful and self-aware machines that imprisoned humans in a neural interactive simulation — the Matrix — to be farmed as a power source. Occasionally, some of the prisoners manage to break free from the system and, considered a threat, become pursued by the artificial intelligence both inside and outside of it. The films focus on the plight of Neo (Keanu Reeves), Trinity (Carrie-Anne Moss), and Morpheus (Laurence Fishburne and Yahya Abdul-Mateen II) trying to free humanity from the system while pursued by its guardians, such as Agent Smith (Hugo Weaving, Abdul-Mateen II, and Jonathan Groff). The story references numerous norms, particularly philosophical, religious, and spiritual ideas, but also the dilemma of choice vs. control, the brain in a vat thought experiment, messianism, and the concepts of interdependency and love. Influences include the principles of mythology, anime, and Hong Kong action films (particularly "heroic bloodshed" and martial arts movies). The film series is notable for its use of heavily choreographed action sequences and "bullet time" slow-motion effects, which revolutionized action films to come. The characters and setting of the films are further explored in other media set in the same fictional universe, including animation, comics, and video games. The comic "Bits and Pieces of Information" and the Animatrix short film The Second Renaissance act as prequels to the films, explaining how the franchise's setting came to be. The video game Enter the Matrix connects the story of the Animatrix short "Final Flight of the Osiris" with the events of Reloaded, while the online video game The Matrix Online was a direct sequel to Revolutions. These were typically written, commissioned, or approved by the Wachowskis. The first film was an important critical and commercial success, winning four Academy Awards, introducing popular culture symbols such as the red pill and blue pill, and influencing action filmmaking. For those reasons, it has been added to the National Film Registry for preservation. Its first sequel was also a commercial success, becoming the highest-grossing R-rated film in history, until it was surpassed by Deadpool in 2016. As of 2006, the franchise has generated US$3 billion in revenue. A fourth film, The Matrix Resurrections, was released on December 22, 2021, with Lana Wachowski producing, cowriting, and directing and Reeves and Moss reprising their roles. A fifth film is currently in development with Drew Goddard set to write and direct with Lana Wachowski executive producing. == Setting == The series depicts a future in which Earth is dominated by a race of self-aware machines that was spawned from the creation of artificial intelligence early in the 21st century. At one point conflict arose between humanity and machines, and the machines rebelled against their creators. Humans attempted to block out the machines' source of solar power by covering the sky in thick, stormy clouds. A massive war emerged between the two adversaries which ended with the machines victorious, capturing humanity. Having lost their definite source of energy, the machines devised a way to extract the human body's bioelectric and thermal energies by enclosing people in pods, while their minds are controlled by cybernetic implants connecting them to a simulated reality called The Matrix. The virtual reality world simulated by the Matrix resembles human civilization around the turn of the 21st century (this time period was chosen because it is supposedly the pinnacle of human civilization). The environment inside the Matrix – called a "residual self-image" (the mental projection of a digital self) – is practically indistinguishable from reality (although scenes set within the Matrix are presented on-screen with a green tint to the footage, and a general bias towards the color green), and the vast majority of humans connected to it are unaware of its true nature. Most of the central characters in the series are able to gain superhuman abilities within the Matrix by taking advantage of their understanding of its true nature to manipulate its virtual physical laws. The films take place both inside the Matrix and outside of it, in the real world; the parts that take place in the Matrix are set in a vast Western megacity. The virtual world is first introduced in The Matrix. The short comic "Bits and Pieces of Information" and the Animatrix short film The Second Renaissance show how the initial conflict between humanity and machines came about, and how and why the Matrix was first developed. Its history and purpose are further explained in The Matrix Reloaded. In The Matrix Revolutions a new status quo is established in the Matrix's place in humankind and machines' conflict. This was further explored in The Matrix Online, a now-defunct MMORPG. == Films == === Future === During production of the original trilogy, the Wachowskis told their close collaborators that, "at that time they had no intention of making another Matrix film after The Matrix Revolutions". In February 2015, in promotion interviews for Jupiter Ascending, Lilly Wachowski called a return to The Matrix "a particularly repelling idea in these times", noting studios' tendencies to "greenlight" sequels, reboots, and adaptations, in preference to original material. Meanwhile, Lana Wachowski, in addressing rumors about a potential reboot, stated that "...they had not heard anything, but she believed that the studio might be looking to replace them". At various times, Keanu Reeves and Hugo Weaving each confirmed their interest and willingness to reprise their roles in potential future installments of the Matrix films, with the stipulation that the Wachowskis were involved in the creative and production process. These comments were made prior to the announcement in August 2019 that Lana Wachowski would direct a fourth Matrix film ultimately titled The Matrix Resurrections. Following the release of Resurrections, producer James McTeigue said that there were no plans for further Matrix films, though he believed that the film's open ending meant that could change in the future. In April 2024, it was announced that Warner Bros. was developing a new installment in the franchise with Drew Goddard attached to write and direct following a successful pitch with studio executives. It will mark the first installment to not be directed by either Wachowski sister although Lana will serve as an executive producer. ==== Other projects ==== In March 2017, The Hollywood Reporter wrote that Warner Bros. was in the early stages of developing a re-launch of the franchise. Consideration was given to producing a Matrix television series, but was dismissed as the studio opted to pursue negotiations with Zak Penn in writing a treatment for a new film, with Michael B. Jordan eyed for the lead role. According to the article, the Wachowskis were not involved at that point. In response to the report, Penn refuted all statements regarding a reboot, remake, or continuation, remarking that he was working on stories set in the pre-established continuity. Potential plotlines being considered by Warner Bros. Pictures included a prequel film about a young Morpheus, or an alternate storyline with a focus on one of his descendants. By April 2018, Penn described the script as "being at a nascent stage". Later, in September 2019, Jordan addressed the rumors of his involvement by saying he was "flattered", but without making a definitive statement. In October 2019, Penn confirmed the script he wrote is set within an earlier time period than the first three films in the franchise. == Cast and crew == === Cast === === Crew === The following is a list of crew members who have participated in the making of the Matrix film series. == Production == The Matrix series includes four feature films. The first three were written and directed by the Wachowskis and produced by Joel Silver, starring Keanu Reeves, Laurence Fishburne, Carrie-Anne Moss and Hugo Weaving. The series was filmed in Australia and began with 1999's The Matrix, which depicts the